Print Email Facebook Twitter Automatic 3D illumination-diagnosis method for large-N arrays: robust data scanner and machine-learning feature provider Title Automatic 3D illumination-diagnosis method for large-N arrays: robust data scanner and machine-learning feature provider Author Chamarczuk, M. (Polish Academy of Sciences) Malinowski, M. (Polish Academy of Sciences) Nishitsuji, Y. (TU Delft Applied Geophysics and Petrophysics) Thorbecke, J.W. (TU Delft Applied Geophysics and Petrophysics) Koivisto, E. (Viikki Biocenter 1) Heinonen, S. (Geological Survey of Finland) Juurela, S. (Boliden FinnEx) Mężyk, M. (Polish Academy of Sciences) Draganov, D.S. (TU Delft Applied Geophysics and Petrophysics) Date 2019 Abstract The main issues related to passive-source reflection imaging with seismic interferometry (SI) are inadequate acquisition parameters for sufficient spatial wavefield sampling and vulnerability of surface arrays to the dominant influence of the omnipresent surface-wave sources. Additionally, long recordings provide large data volumes that require robust and efficient processing methods. We address these problems by developing a two-step wavefield evaluation and event detection (TWEED) method of body waves in recorded ambient noise. TWEED evaluates the spatiotemporal characteristics of noise recordings by simultaneous analysis of adjacent receiver lines. We test our method on synthetic data representing transient ambient-noise sources at the surface and in the deeper subsurface. We discriminate between basic types of seismic events by using three adjacent receiver lines. Subsequently, we apply TWEED to 600 h of ambient noise acquired with an approximately 1000-receiver array deployed over an active underground mine in Eastern Finland. We develop the detection of body-wave events related to mine blasts and other routine mining activities using a representative 1 h noise panel. Using TWEED, we successfully detect 1093 body-wave events in the full data set. To increase the computational efficiency, we use slowness parameters derived from the first step of TWEED as input to a support vector machine (SVM) algorithm. Using this approach, we detect 94% of the TWEED-evaluated body-wave events indicating the possibility to limit the illumination analysis to only one step, and therefore increase the time efficiency at the price of lower detection rate. However, TWEED on a small volume of the recorded data followed by SVM on the rest of the data could be efficiently used for a quick and robust (real-time) scanning for body-wave energy in large data volumes for subsequent application of SI for retrieval of reflections. Subject arraysilluminationimaginginterferometrymining To reference this document use: http://resolver.tudelft.nl/uuid:16323c65-5524-4ee3-8401-f679fbd95eb4 DOI https://doi.org/10.1190/geo2018-0504.1 ISSN 0016-8033 Source Geophysics, 84 (3), Q13-Q25 Part of collection Institutional Repository Document type journal article Rights © 2019 M. Chamarczuk, M. Malinowski, Y. Nishitsuji, J.W. Thorbecke, E. Koivisto, S. Heinonen, S. Juurela, M. Mężyk, D.S. Draganov Files PDF TWEED_R2_submission.pdf 2.4 MB Close viewer /islandora/object/uuid:16323c65-5524-4ee3-8401-f679fbd95eb4/datastream/OBJ/view